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Source note3 min read

Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis

Source

arXiv

arxiv.org/abs/2608.11006v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Benjamin Faveri, Brie Bhasin
  • Venue: arXiv: cs.CY
  • Published: 2026-08-11
  • Collected: 2026-08-12
  • URL: http://arxiv.org/abs/2608.11006v1
  • PDF: https://arxiv.org/pdf/2608.11006v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper analyzes national and regional AI strategies of various countries, finding strong horizontal convergence in economic competitiveness, research support, and ethical AI use. However, there is persistent divergence in human rights goals, participatory governance approaches, and human-centric principles. The African Union (AU) exhibits the highest vertical convergence, while the EU shows strong alignment on regulatory and economic priorities but diverges on human-centric values. The Nordic-Baltic Region displays mixed vertical convergence.

What the Document Says

  • 74 national AI strategies and 3 regional AI strategies were analyzed.
  • The analysis used a latent-inductive approach organized around three functional policy design elements: goals, approaches, and principles.
  • The research questions focused on the horizontal and vertical convergence/divergence of national AI strategies over time and with respective regional AI strategies.
  • Strong horizontal convergence was found in economic competitiveness, research support, and ethical AI use. Persistent divergence was found in human rights goals, participatory governance approaches, and human-centric principles.
  • Across the three regions, the AU exhibited the highest vertical convergence, the EU demonstrated strong alignment on regulatory and economic priorities but diverged on human-centric values, and the Nordic-Baltic Region displayed mixed vertical convergence.

Key Concepts Mentioned

  • AI strategies
  • policy design elements
  • goals
  • approaches
  • principles
  • horizontal convergence
  • vertical convergence
  • economic competitiveness
  • research support
  • ethical AI use
  • human rights
  • participatory governance
  • human-centric principles
  • African Union (AU)
  • European Union (EU)
  • Nordic-Baltic Region

Claims Made by the Document

  • UNKNOWN

Evidence Used

  • 74 national AI strategies and 3 regional AI strategies were analyzed.
  • Results indicate strong horizontal convergence... Persistent divergence... Across the three regions...

Methods / Approach

  • latent-inductive approach
  • coding

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

Data may be missing from countries that did not publish AI strategies or are not part of the UN.

Open Questions

  • Are there any common policy design elements that diverge between regional strategies?
  • How do industry and private sector initiatives in AI align with national and regional strategies?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not address commonalities or differences between regional strategies, nor does it consider the alignment of industry and private sector initiatives with national and regional strategies.

Raw Abstract / Excerpt

Governments worldwide have responded to the rapid expansion of AI by publishing national and regional AI strategies. Comparing national and regional AI strategies to identify their convergences and divergences can uncover their common practices, understand regional variations, and provide policy designers a comprehensive set of policy design elements for their ongoing AI strategy developments. Yet, existing work has not examined their underlying policy design elements or assessed whether those elements are horizontally (country-to-country) or vertical (region-to-country) converging or diverging over time. This paper addresses that gap by coding and analyzing 74 national and 3 regional AI strategies drawn from a global scan of all 205 UN member and non-member states. The coding used a latent-inductive approach organized around three functional policy design elements: goals, approaches, and principles. Two research questions guided the analysis: to what degree are national AI strategies becoming horizontally convergent or divergent over time; and to what degree are national strategies becoming vertically convergent or divergent with those countries' regional AI strategy. Results indicate strong horizontal convergence around economic competitiveness, research support, and ethical AI use, alongside persistent divergence in human rights goals, participatory governance approaches, and human-centric principles. Across the three regions, the AU exhibits the highest vertical convergence, the EU demonstrated strong alignment on regulatory and economic priorities but diverges on human-centric values, and the Nordic-Baltic Region displays mixed vertical convergence. These findings offer policy designers a comprehensive evidence base for identifying emerging AI policy design choice norms as AI strategies are developed and updated.